Pre-Processing Techniques applied on mutual funds
DOI:
https://doi.org/10.22399/ijcesen.2843Keywords:
Data Pre-Processing, Mutual Funds, Data Mining, NormalizationAbstract
The raw data obtained from the logs may be noisy, incomplete, and inconsistent that’s why data pre-processing is an essential step in data mining. The quality of data plays a vital role during the evaluation process. The results of the evaluation process primarily depend upon the quality of the data input. So, data pre-processing is the primary and most crucial step before knowledge discovery. This paper is based on two main steps- data pre-processing techniques and results after applied data pre-processing on mutual funds’ data. Data pre-processing transforms the raw data into a structured, understandable format. Moreover, data pre-processing performs not only the transformation of data but also makes it understandable according to need. It is mainly divided into four steps, i.e., data integration, data cleaning, data transformation, and data reduction. This paper takes the fifteen-year NAV data of twenty mutual funds for analysis propose. This paper explains several techniques of data pre-processing to transform the raw data into an understandable format.
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